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Yeni Kustiyahningsih
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Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
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Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 157 Documents
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APPLICATION OF COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION Rizki Anantama
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.264

Abstract

Pneumonia is a disease caused by a viral, bacterial, or fungal infection. In the diagnostic process of pneumonia, one approach is to use X-ray images. One of the existing problems is the lack of qualified and experienced medical personnel to recognize the X-ray images that have been taken. For this reason, an alternative is needed to detect pneumonia. Existing research shows that the use of convolutional neural networks can effectively detect pneumonia X-ray images. However, one of the problems is that this approach focuses a lot on accuracy without considering performance criteria such as sensitivity and specificity. To solve this problem, a cost-sensitive based approach has been proposed. In this study, a convolutional neural network-based model was created and trained using a cost-sensitive and non-cost sensitive approach. From the results obtained, it is seen that the model made still has a comparatively low level of accuracy. However, it is found that training with a cost-sensitive approach is able to improve performance on the specificity side, although at the expense of performance on the sensitivity side.
OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD Hendy Prasetyo; Trihastuti Agustinah
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.267

Abstract

Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic obstacles is. Avoidance direction is determined by considering nearest distance based on the dimensions of the obstacle. Due to limited battery capacity, the quadcopter also needs to consider energy efficiency in obstacle avoidance. The obstacle’s properties and movement direction are also needed in considering the correct avoidance direction. Using a modified Local Mean K-Nearest Centroid Neighbor (LMKNCN) algorithm results in a 97.5% accuracy for avoidance direction decision. The learning process between training data and testing data yielded a computation duration of 0.142341 seconds. The simulations showed that the quadcopter is able to avoid static and dynamic obstacles to reach its destination without collisions.
DEVELOPMENT OF AUTONOMOUS UNDERWATER VEHICLE (AUV) BASED ON ROBOTIC OPERATING SYSTEM FOR FOLLOWING UNDERWATER CABLE Mardiyanto Ronny
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.274

Abstract

In addition to satellites, the Marine Cable Communication Channel (SKKL) which is located under the sea is also one of the backbones of the communication network to connect from one island to another. However, there are often irresponsible parties who damage or commit acts of vandalism. This action resulted in the disruption of the communication process on the submarine cable network. So, it is necessary to periodically check the condition of the underwater communication network cables. Regular checking of underwater cables is very risky, so we propose an underwater robot to handle it. This paper presents the development of Autonomous Underwater Vehicle (AUV) based on Robotic Operating System (ROS) for Following Underwater Cable to monitor the condition of the cables automatically. The AUV is equipped with a camera, Nvidia Jetson Nano, Arduino, Flight Controller, ESC, and brushless DC motors that used to assist the tracking process on the cable. The camera is used as the main visual sensor. Visual image processing methods are carried out using thresholding and contours detection methods, then the obtained data are processed to drive the motors on the AUV so that they can move on the direction of the cable. The experiment results show that the object detection method can be used under conditions with light intensity more than 25 lux. It works optimally at speeds of 0.27 m/s to 0.42 m/s. In the horizontal motion control test, the overshoot parameter value is ±60%, rise time is 2s, settling time is 16s, and steady state error is ±20%. The AUV can track on a straight and winding path of 2 meters with a bright light intensity of ±493 lux, a dim light of ±107 lux and a dark light intensity of ±25 lux with the help of an LED beam with a light intensity of ±773 lux. The percentage of success of scoping experiments on a straight track and a winding track with three trials is 75%. This performance shows that the developed AUV works well to follow underwater cable.
ANT COLONY OPTIMIZATION TO DETERMINE THE SHORTEST ROUTE OF TOURIST DESTINATIONS IN BALI : A CASE STUDY I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.279

Abstract

Bali is one of the popular tourist destinations in Indonesia. As technology develops, the tourism sector also gets support in the process. The problem we address in this research is to determine the closest distance to travel in Bali. The purpose of this study is to assist users in deciding which route is better to visit first so that it is more effective in terms of energy, gasoline usage, and time. We proposed a shortest route system using ant colony optimization (ACO). ACO then compared with other optimization method. ACO produces a great result with the optimal distance and reasonable amount of search time.
EFFECTIVENESS OF DEEP LEARNING APPROACH FOR TEXT CLASSIFICATION IN ADAPTIVE LEARNING Umi Laili Yuhana; Imamah Imamah; Chastine Fatichah; Bagus Jati Santoso
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.285

Abstract

Klasifikasi text sangat bermanfaat dan dibutuhkan diberbagai bidang. Salah satu bidang yang membutuhkan klasifikasi text adalah E-learning yang bersifat adaptive atau disebut sebagai adaptive learning sistem. Adaptive learning sytem adalah sistem pembelajaran online yang dapat memberikan rekomendasi pembelajaran berdasarkan kebutuhan pengguna. Adaptive learning memiliki dua bagian, yaitu modul learning dan modul testing. Modul learning adalah bagian dari sistem yang bertugas untuk memberikan rekomendasi pembelajaran bagi pengguna, sedangkan modul testing bertugas untuk menguji dan memberikan penilaian terhadap hasil pembelajaran yang diperoleh dari modul learning. Materi pembelajaran pada modul learning memerlukan klasifikasi text berdasarkan tingkat kesulitannya untuk memastikan bahwa pengguna dengan level kemampuan rendah juga mendapatkan materi pembelajaran yang mudah, dan rekomendasi ini akan dinamis mengikuti perkembangan kemampuan pengguna. Pada penelitian ini, akan dibahas bagian kecil dari sistem pembelajaran adaptive pada modul learning yaitu tahap klasifikasi text. Dataset yang digunakan dalam penelitian ini adalah mata pelajaran IPA untuk tingkat SMP yang didapatkan dari Ruang Guru dan merupakan salah satu platform E-learning di Indonesia. Metode yang digunakan dalam penelitian ini adalah CNN, RNN dan HAN dengan menggunakan word embedding Word2Vec
ANALYSIS OF FATIGUE AMONG BAGGAGE HANDLERS USING THE FACIAL ACTION CODING SYSTEM (FACS) Samuel Goesniady; Wilma Latuny
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.308

Abstract

Baggage workers is a livelihood in the informal sector which is carried out by selling services to transport goods/materials from one place to another. The workload carried by the workers will determine how much income they receive in a day. For this reason, baggage workers tend to scramble to transport goods to earn more income, so sometimes baggage workers will carry goods beyond their capacity. The Facial Action Coding System (FACS) is a comprehensive anatomy-based system for visually describing all visible facial movements. It breaks down facial expressions into individual components of muscle movement, called Action Units (AUs). Given the fact that FACS can identify fatigue by analyzing human facial expressions, the authors took the initiative to explore the facial expressions of workers' fatigue with FACS automation on CERT. In this research, it was found that there was an indication of fatigue in baggage workers. This can be seen from the significant changes in the value of action units indicating fatigue for baggage workers before and after work, including an increase in the value of AU 01, AU 15, AU 20 and AU 23, while the AU 12 which is identical to the smile expression experienced a decrease in intensity in most of the subjects and the different test results from the five Action Units showed that there were significant differences between the Action Units before and after work.
CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD I Made Satria Bimantara; I Wayan Supriana
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.268

Abstract

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds.
OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES I Made Satria Bimantara; I Made Widiartha
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.269

Abstract

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.
SLEEP DISORDER IDENTIFICATION FROM SINGLE LEAD ECG BY IMPROVING HYPERPARAMETERS OF 1D-CNN Iman Fahruzi
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.302

Abstract

Disruption of the flow of breathing during sleep will result in significant heart problems if not treated seriously. An electrocardiogram (ECG) recording is one of the most used methods for detecting sleep disorders early on. An ECG is a representation of electrical activity in the heart while it is beating. The irregularities of the morphology and the complexity of the recordings have clinical significance that can be used as a tool for diagnosing sleep disorders. This study uses engineering to obtain features from ECG recordings that are carried out automatically using deep learning machine learning with a Convolutional Neural Network (CNN) model approach. The ECG recordings were processed to remove noise before being used in the CNN model. Tests are carried out on the most optimal model to get good accuracy by applying two scenarios. The test results of the two scenarios show that scenario one has an accuracy of 83.03% compared to scenario two with an accuracy of 76.88%. Meanwhile, the precision, sensitivity, cohens kappa and ROC UAC levels were 81.78%, 87.78%, 65.73% and 82.68% in scenario one testing on the CNN model with the most optimal parameter settings, respectively
APPLICATION OF MULTISTAGE CLUSTERING FOR MAPPING ECONOMIC POTENTIAL IN EAST JAVA PROVINCE Ronny Susetyoko; Edi Satriyanto; Alfi Fadliana; Muhammad Syahfitra
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.325

Abstract

This study aims to map the economic potential in East Java Province based on GRDP according to business field category. Multistage clustering is a method developed for outlier data and datasets with large variance. Multistage clustering is a combination of Ordering Points to Identify the Clustering Structure (OPTICS) and K-Means. The first stage was grouped using OPTICS. The outlier data resulting from the clustering stage is used as a dataset in the second stage using K-Means. The performance of this method is compared with several other methods, namely: K-Means, DBSCAN – K-Means, Agglomerative, Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), and Fuzzy Possibilistic C-Means (FPCM) based on the characteristics of the Silhouette score and Davies-Bouldin score. Multistage clustering was chosen as the best method with a Silhouette score of 0.442 and Davies-Bouldin score of 0.388. With the Elbow method and the two metrics, the optimum number of clusters is 8 clusters. The results of this mapping method, the City of Surabaya forms a separate cluster which has the highest economic potential in 15 categories of business fields. Next Gresik, Pasuruan, Sidoarjo, and Probolinggo have the second highest economic potential with 10 categories of business fields ranking in the top 3.

Page 13 of 16 | Total Record : 157

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